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Sayyed SK, Quraishi M, Jobby R, Rameshkumar N, Kayalvizhi N, Krishnan M, Sonawane T. A computational overview of integrase strand transfer inhibitors (INSTIs) against emerging and evolving drug-resistant HIV-1 integrase mutants. Arch Microbiol 2023; 205:142. [PMID: 36966200 PMCID: PMC10039815 DOI: 10.1007/s00203-023-03461-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 03/27/2023]
Abstract
AIDS (Acquired immunodeficiency syndrome) is one of the chronic and potentially life-threatening epidemics across the world. Hitherto, the non-existence of definitive drugs that could completely cure the Human immunodeficiency virus (HIV) implies an urgent necessity for the discovery of novel anti-HIV agents. Since integration is the most crucial stage in retroviral replication, hindering it can inhibit overall viral transmission. The 5 FDA-approved integrase inhibitors were computationally investigated, especially owing to the rising multiple mutations against their susceptibility. This comparative study will open new possibilities to guide the rational design of novel lead compounds for antiretroviral therapies (ARTs), more specifically the structure-based design of novel Integrase strand transfer inhibitors (INSTIs) that may possess a better resistance profile than present drugs. Further, we have discussed potent anti-HIV natural compounds and their interactions as an alternative approach, recommending the urgent need to tap into the rich vein of indigenous knowledge for reverse pharmacology. Moreover, herein, we discuss existing evidence that might change in the near future.
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Affiliation(s)
- Sharif Karim Sayyed
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India
| | - Marzuqa Quraishi
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India
| | - Renitta Jobby
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India
| | | | - Nagarajan Kayalvizhi
- Regenerative Medicine Laboratory, Department of Zoology, School of Life Sciences, Periyar University, Salem, Tamil Nadu, 636011, India
| | | | - Tareeka Sonawane
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India.
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Shinde PB, Bhowmick S, Alfantoukh E, Patil PC, Wabaidur SM, Chikhale RV, Islam MA. De novo design based identification of potential HIV-1 integrase inhibitors: A pharmacoinformatics study. Comput Biol Chem 2020; 88:107319. [PMID: 32801062 DOI: 10.1016/j.compbiolchem.2020.107319] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/10/2020] [Accepted: 06/22/2020] [Indexed: 12/30/2022]
Abstract
In the present study, pharmacoinformatics paradigms include receptor-based de novo design, virtual screening through molecular docking and molecular dynamics (MD) simulation are implemented to identify novel and promising HIV-1 integrase inhibitors. The de novodrug/ligand/molecule design is a powerful and effective approach to design a large number of novel and structurally diverse compounds with the required pharmacological profiles. A crystal structure of HIV-1 integrase bound with standard inhibitor BI-224436 is used and a set of 80,000 compounds through the de novo approach in LigBuilder is designed. Initially, a number of criteria including molecular docking, in-silico toxicity and pharmacokinetics profile assessments are implied to reduce the chemical space. Finally, four de novo designed molecules are proposed as potential HIV-1 integrase inhibitors based on comparative analyses. Notably, strong binding interactions have been identified between a few newly identified catalytic amino acid residues and proposed HIV-1 integrase inhibitors. For evaluation of the dynamic stability of the protein-ligand complexes, a number of parameters are explored from the 100 ns MD simulation study. The MD simulation study suggested that proposed molecules efficiently retained their molecular interaction and structural integrity inside the HIV-1 integrase. The binding free energy is calculated through the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach for all complexes and it also explains their thermodynamic stability. Hence, proposed molecules through de novo design might be critical to inhibiting the HIV-1 integrase.
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Affiliation(s)
- Pooja Balasaheb Shinde
- Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth Deemed University, Pune-Satara Road, Pune, India
| | - Shovonlal Bhowmick
- Department of Chemical Technology, University of Calcutta, 92, A.P.C. Road, Kolkata, 700009, India
| | - Etidal Alfantoukh
- Health Sciences Research Center, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Pritee Chunarkar Patil
- Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth Deemed University, Pune-Satara Road, Pune, India
| | - Saikh Mohammad Wabaidur
- Department of Chemistry P.O. Box 2455, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Rupesh V Chikhale
- School of Pharmacy, University of East Anglia, Norwich, United Kingdom
| | - Md Ataul Islam
- Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom; School of Health Sciences, University of Kwazulu-Natal, Westville Campus, Durban, South Africa; Department of Chemical Pathology, Faculty of Health Sciences, University of Pretoria and National Health Laboratory Service Tshwane Academic Division, Pretoria, South Africa.
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Patel S, Patel B, Bhatt H. 3D-QSAR studies on 5-hydroxy-6-oxo-1,6-dihydropyrimidine-4- carboxamide derivatives as HIV-1 integrase inhibitors. J Taiwan Inst Chem Eng 2016. [DOI: 10.1016/j.jtice.2015.07.024] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Masso M. Modeling functional changes to Escherichia coli thymidylate synthase upon single residue replacements: a structure-based approach. PeerJ 2015; 3:e721. [PMID: 25648456 PMCID: PMC4304848 DOI: 10.7717/peerj.721] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Accepted: 12/18/2014] [Indexed: 11/30/2022] Open
Abstract
Escherichia coli thymidylate synthase (TS) is an enzyme that is indispensable to DNA synthesis and cell division, as it provides the only de novo source of dTMP by catalyzing the reductive methylation of dUMP, thus making it a key target for chemotherapeutic agents. High resolution X-ray crystallographic structures are available for TS and, owing to its relatively small size, successful experimental mutagenesis studies have been conducted on the enzyme. In this study, an in silico mutagenesis technique is used to investigate the effects of single amino acid substitutions in TS on enzymatic activity, one that employs the TS protein structure as well as a knowledge-based, four-body statistical potential. For every single residue TS variant, this approach yields both a global structural perturbation score and a set of local environmental perturbation scores that characterize the mutated position as well as all structurally neighboring residues. Global scores for the TS variants are capable of uniquely characterizing groups of residue positions in the enzyme according to their physicochemical, functional, or structural properties. Additionally, these global scores elucidate a statistically significant structure–function relationship among a collection of 372 single residue TS variants whose activity levels have been experimentally determined. Predictive models of TS variant activity are subsequently trained on this dataset of experimental mutants, whose respective feature vectors encode information regarding the mutated position as well as its six nearest residue neighbors in the TS structure, including their environmental perturbation scores.
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Affiliation(s)
- Majid Masso
- Laboratory for Structural Bioinformatics, School of Systems Biology, George Mason University , Manassas, VA , USA
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